As of August 2026,
Gambling Change Persons and Booth Cashiers has an AI-exposure score of 63/100
(High exposure) on the AI-Safe Careers index. This is an estimate of task
exposure, not a prediction of job loss.
Score inputs for this role:
This role starts from O*NET 29.1 occupational descriptors and is empirically grounded by Penn/OpenAI GPTs are GPTs study, Anthropic Economic Index (June 26, 2026), Felten, Raj and Seamans AIOE index. BLS labor-market figures are separate context, not score inputs.
Gambling Change Persons and Booth Cashiers
More exposed than 75% of the roles we track. Median pay ~US$36,220. About 4,000 projected openings a year (BLS 2024–34 - growth plus replacement).
Pay & demand figures are US medians (BLS, in USD) - your local figures will differ. Your exposure score applies broadly.
Will AI replace Gambling Change Persons and Booth Cashiers?
No exposure score can predict whether AI will replace this role. The 63/100 score means our current model estimates high task exposure from the sources listed on this page. It does not predict an employer decision, headcount, or an individual outcome. The task map below shows the work assessed and where human judgment remains important.
Early-career context (study ages 22-25)
This role's High score and automation-heavy task mix point in the same direction as the study's highest-exposure automation cohort. The measures are not identical, so the study result is context rather than a direct forecast for this role.
The November 2025 revision reports a 16% relative employment decline for workers ages 22-25 in the most AI-exposed U.S. occupations, after firm-level controls, relative to workers in less-exposed fields and more experienced workers in the same occupations.
Use the task map to identify durable work and the skills worth building early.
Stanford Digital Economy Lab, Canaries in the Coal Mine - November 2025 revision
This is group-level U.S. payroll evidence, not a personal forecast. The authors do not claim that AI alone caused the change, and this context does not alter the exposure score.
How this role compares to similar Sales roles
What this role usually involves
Exchange coins, tokens, and chips for patrons' money. May issue payoffs and obtain customer's signature on receipt. May operate a booth in the slot machine area and furnish change persons with money bank at the start of the shift, or count and audit money in drawers.
Broad guidance for this preparation level; exact requirements vary by role and employer.
Source: O*NET 29.1 exact occupation - Gambling Change Persons and Booth Cashiers, SOC 41-2012.00. Context describes the role; the AI-exposure score remains a separate task-exposure estimate.
Gambling Change Persons and Booth Cashiers tasks, by AI exposure
- Calculate the value of chips won or lost by players.
- Reconcile daily summaries of transactions to balance books.
- Furnish change persons with a money bank at the start of each shift.
No augmentable tasks identified for this role - all 13 of its individually-assessed tasks read as automatable.
No durable tasks identified for this role - all 13 of its individually-assessed tasks read as automatable.
We analyzed all 13 Gambling Change Persons and Booth Cashiers tasks - 13 automatable. The full task map - every task with exactly what to do about each - is in your Career Report.
Your report unlocks three concrete artifacts
Every task scored with what to automate, augment, or protect.
Related roles with exposure deltas, salary, demand, and reachability. Lower-exposure options appear only when the data supports them.
A keepable roadmap plus resume and LinkedIn repositioning.
Grounded in O*NET-linked or curated role data, with Penn, Anthropic Economic Index, and AIOE signals where matched. BLS labor-market context is separate - not generic advice.
Adjacent career paths
Relatedness shows how reachable a move may be. A path is labeled lower exposure only when its score is at least 6 points lower; every row shows the measured difference.
Calculate the value of chips won or lost by players.
Your AI-Safe Career Report
Every task scored with what to do about it; adjacent paths with honest exposure deltas, salary, demand, and reachability; a skill-gap map; a 30/60/90-day roadmap; Agent Reality Check; plus a résumé and LinkedIn rewrite and professional PDF.
Grounded in O*NET-linked or curated role data, with Penn, Anthropic Economic Index, and AIOE signals where matched. BLS labor-market context is separate.
AI was the most-cited reason for U.S. layoffs through mid-2026 - the workers who adapt earliest fare best. - Challenger, Gray & Christmas, 2026The upside: Workers with AI skills earn a roughly 62% wage premium - adapting pays. - PwC Global AI Jobs Barometer, 2026
Generation starts after checkout; reports are typically ready within a few minutes.
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Gambling Change Persons and Booth Cashiers - median pay by US state (BLS OEWS, USD)
Median annual wage, in USD. US national: US$36,220. More states are being added.